Sentiment Normalization Based on Current Authors Personality Insight Data Points

ABSTRACT

An approach is provided that analyzes electronic document sets, each of the sets written by a different author. The analysis includes performing a normalized sentiment analysis of the documents which results in normalized sentiment scores that pertain to each of the authors. The normalize sentiment scores are stored in a data store that is accessible from a question answering (QA) system. The question answering system then receives a sentiment-based question. Responsively, the QA system generates a qualitative set of candidate answers, with the candidate answers based at least in part on the normalized sentiment scores retrieved from the data store.

BACKGROUND OF THE INVENTION Technical Field

This disclosure relates to normalizing sentiment derived from variousauthors.

Description of Related Art

Sentiment analysis involves using Natural Language Processing (NLP)techniques to identify and extract subjective information in text underconsideration. Such sentiment analysis data is often used when ingestingdata into a corpus utilized by a question answering (QA) system.Existing techniques return sentiment polarity (e.g., positive, negative,or neutral, etc.) in an overall source document or at a more granularlevel, such as at an entity level. These techniques return a numericalscore for the sentiment to indicate the strength or weakness of thesentiment. However, none of the traditional techniques of sentimentanalysis take into consideration the personality traits of theindividual expressing the sentiment. In addition, none of thetraditional techniques of sentiment analysis take into consideration thechanging nature of the individual expressing the sentiment over time,often based on the individual's life-experiences over time.

SUMMARY

An approach is provided that analyzes electronic document sets, each ofthe sets written by a different author. The analysis includes performinga normalized sentiment analysis of the documents which results innormalized sentiment scores that pertain to each of the authors. Thenormalize sentiment scores are stored in a data store that is accessiblefrom a question answering (QA) system. The question answering systemthen receives a sentiment-based question. Responsively, the QA systemgenerates a qualitative set of candidate answers, with the candidateanswers based at least in part on the normalized sentiment scoresretrieved from the data store.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present inventionwill be apparent in the non-limiting detailed description set forthbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a block diagram of a processor and components of aninformation handling system;

FIG. 2 is a network environment that includes various types ofinformation handling systems interconnected via a computer network;

FIG. 3 is a component diagram that depicts the interaction of componentsused in providing sentiment normalization based on current authorspersonality insight data points;

FIG. 4 is a depiction of data flows to users of a question-answering(QA) system while providing sentiment normalization;

FIG. 5 is a depiction of a QA system pipeline utilized in providingqualitative answers based on normalized sentiment scores included in theQA system corpus;

FIG. 6 is a depiction of the clustering of personality profile insights;

FIG. 7 is a depiction of a flowchart showing the logic used to analyzeand normalize sentiment data utilized in the QA system;

FIG. 8 is a depiction of a flowchart showing the logic used to generateuser profile data over different periods of time;

FIG. 9 is a depiction of a flowchart showing the logic used to identifyauthor's group personality sentiments over different time periods; and

FIG. 10 is a depiction of a flowchart showing the logic used tonormalize sentiment scores utilized by the QA system.

DETAILED DESCRIPTION

FIGS. 1-10 describe an approach that relates to sentiment analysis oftext. Specifically, this approach provides a method for personalizingsentiment scoring for an author. A core aspect of the approach is thenormalization of a sentiment score for an author based on an analysis ofdocuments from the author over a period of time. The idea is to accountfor the fact that certain authors may simply be overly positive oroverly negative about an entity, concept or topic over a period of time.For example, supporters for a given political candidate may be generallypositive about that political candidate, thereby making it difficult toisolate documents (i.e. social media posts) that are more positive thanusual. Likewise, supporters for an opposing political candidate may begenerally negative about the political candidate over a period of time,thereby making it difficult to isolate documents that are more negativethan usual. This makes it easier to answer queries about reactions toevents amongst persons that generally support the political candidate,or generally dislike a political candidate. The same can be applied toany entity, concept or topic, such as sports, international conflicts,etc. The approach operates by receiving a corpus of documents from anauthor (for example, posts and articles from the author), extracting therelevant entities, concepts and topics, and for each topic, identify acollection of relevant documents, sort the documents by time, andcompute the tone, or sentiment, for the collection of documents overtime. Once the sentiment over time for a topic for an author isdetermined, it is stored and used to normalize the sentiment of anyspecific document from that author. In addition, an approach is providedto identify an author's sentiment during a particular period of time.For example, an author may be a supporter of one political party duringthe 1990s, and a different political party during the 2000s. Based uponthe date of a document by the author, the sentiment that the author wasconveying during that period of time can be used, rather than an overallsentiment of the author during all periods of time.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following detailed description will generally follow the summary ofthe invention, as set forth above, further explaining and expanding thedefinitions of the various aspects and embodiments of the invention asnecessary. To this end, this detailed description first sets forth acomputing environment in FIG. 1 that is suitable to implement thesoftware and/or hardware techniques associated with the invention. Anetworked environment is illustrated in FIG. 2 as an extension of thebasic computing environment, to emphasize that modern computingtechniques can be performed across multiple discrete devices.

FIG. 1 illustrates information handling system 100, which is asimplified example of a computer system capable of performing thecomputing operations described herein. Information handling system 100includes one or more processors 110 coupled to processor interface bus112. Processor interface bus 112 connects processors 110 to Northbridge115, which is also known as the Memory Controller Hub (MCH). Northbridge115 connects to system memory 120 and provides a means for processor(s)110 to access the system memory. Graphics controller 125 also connectsto Northbridge 115. In one embodiment, PCI Express bus 118 connectsNorthbridge 115 to graphics controller 125. Graphics controller 125connects to display device 130, such as a computer monitor.

Northbridge 115 and Southbridge 135 connect to each other using bus 119.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 115and Southbridge 135. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 135, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 135typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 196 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (198) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 135 to Trusted Platform Module (TPM) 195.Other components often included in Southbridge 135 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 135to nonvolatile storage device 185, such as a hard disk drive, using bus184.

ExpressCard 155 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 155 supports both PCI Expressand USB connectivity as it connects to Southbridge 135 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 135 includesUSB Controller 140 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 150, infrared(IR) receiver 148, keyboard and trackpad 144, and Bluetooth device 146,which provides for wireless personal area networks (PANs). USBController 140 also provides USB connectivity to other miscellaneous USBconnected devices 142, such as a mouse, removable nonvolatile storagedevice 145, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 145 is shown as a USB-connected device,removable nonvolatile storage device 145 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 175 connects to Southbridge 135via the PCI or PCI Express bus 172. LAN device 175 typically implementsone of the IEEE .802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 100 and another computer system or device.Optical storage device 190 connects to Southbridge 135 using Serial ATA(SATA) bus 188. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 135to other forms of storage devices, such as hard disk drives. Audiocircuitry 160, such as a sound card, connects to Southbridge 135 via bus158. Audio circuitry 160 also provides functionality such as audioline-in and optical digital audio in port 162, optical digital outputand headphone jack 164, internal speakers 166, and internal microphone168. Ethernet controller 170 connects to Southbridge 135 using a bus,such as the PCI or PCI Express bus. Ethernet controller 170 connectsinformation handling system 100 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 1 shows one information handling system, an informationhandling system may take many forms. For example, an informationhandling system may take the form of a desktop, server, portable,laptop, notebook, or other form factor computer or data processingsystem. In addition, an information handling system may take other formfactors such as a personal digital assistant (PDA), a gaming device, ATMmachine, a portable telephone device, a communication device or otherdevices that include a processor and memory.

The Trusted Platform Module (TPM 195) shown in FIG. 1 and describedherein to provide security functions is but one example of a hardwaresecurity module (HSM). Therefore, the TPM described and claimed hereinincludes any type of HSM including, but not limited to, hardwaresecurity devices that conform to the Trusted Computing Groups (TCG)standard, and entitled “Trusted Platform Module (TPM) SpecificationVersion 1.2.” The TPM is a hardware security subsystem that may beincorporated into any number of information handling systems, such asthose outlined in FIG. 2.

FIG. 2 provides an extension of the information handling systemenvironment shown in FIG. 1 to illustrate that the methods describedherein can be performed on a wide variety of information handlingsystems that operate in a networked environment. Types of informationhandling systems range from small handheld devices, such as handheldcomputer/mobile telephone 210 to large mainframe systems, such asmainframe computer 270. Examples of handheld computer 210 includepersonal digital assistants (PDAs), personal entertainment devices, suchas MP3 players, portable televisions, and compact disc players. Otherexamples of information handling systems include pen, or tablet,computer 220, laptop, or notebook, computer 230, workstation 240,personal computer system 250, and server 260. Other types of informationhandling systems that are not individually shown in FIG. 2 arerepresented by information handling system 280. As shown, the variousinformation handling systems can be networked together using computernetwork 200. Types of computer network that can be used to interconnectthe various information handling systems include Local Area Networks(LANs), Wireless Local Area Networks (WLANs), the Internet, the PublicSwitched Telephone Network (PSTN), other wireless networks, and anyother network topology that can be used to interconnect the informationhandling systems. Many of the information handling systems includenonvolatile data stores, such as hard drives and/or nonvolatile memory.Some of the information handling systems shown in FIG. 2 depictsseparate nonvolatile data stores (server 260 utilizes nonvolatile datastore 265, mainframe computer 270 utilizes nonvolatile data store 275,and information handling system 280 utilizes nonvolatile data store285). The nonvolatile data store can be a component that is external tothe various information handling systems or can be internal to one ofthe information handling systems. In addition, removable nonvolatilestorage device 145 can be shared among two or more information handlingsystems using various techniques, such as connecting the removablenonvolatile storage device 145 to a USB port or other connector of theinformation handling systems.

FIG. 3 is a component diagram that depicts the interaction of componentsused in providing sentiment normalization based on current authorspersonality insight data points. At step 310, the document ingestionprocess retrieves data for ingestion into QA system 100 system 100 froma wide variety of electronic documents 300. Document ingestion 310results in documents 320 being ingested into corpus 105 of QA system100. Sentiment analysis process 330 performs a textual analysis of thedocuments being ingested into corpus 106. The result of sentimentanalysis process 330 is a traditional sentiment score that is stored insentiment scores data store 340. Sentiment normalization process 350receives author data pertaining to documents 320 being ingested intocorpus 106. The sentiment normalization process retrieves data fromnetwork sources 102, such as other electronic documents 300 written bythe author, social media site data 375 with posts and other works by theauthor, and other sites, such as blogs and the like, where the authorhas activity or involvement. The text written by the author is analyzedby sentiment normalization process 350 to ascertain the author'spersonality. For example, the analysis may reveal that the author is astrong advocate for a particular political party, so opinions by theauthor supporting the political party or in line with the party'spositions would be expected, while writings by the other not in linewith the party's positions would be unexpected. The sentimentnormalization process develops author personality profiles 360. Theseprofiles can be further grouped, or clustered, based on the personalitytraits indicated in the profiles. The author personality profiles arethen used to normalize sentiment data found in ingested documents. Asentiment expressed by an author that is strongly inline with theauthor's profile is adjusted because of the author's likelihood to havesuch feelings. For example, if the author of text ingested into corpus106 expresses a sentiment strongly disapproving of a particularinternational trade agreement, but the author's profile shows that theauthor is strongly aligned with a political party or position thatstrongly disfavors any international trade agreements, then thesentiment expressed by the author is normalized by adjusting thesentiment from a “strong” sentiment to a less strong sentiment. Bynormalizing sentiments across numerous authors and ingested documents, amore realistic sentiment can be found in the normalized data. Thenormalized sentiment scores are stored in data store 390.

FIG. 4 is a depiction of data flows to users of a question-answering(QA) system while providing sentiment normalization. Users 410, such asauthors, provide written opinions and other reviews 430 that includesentiment-based data. These opinions and reviews are stored in variousnetwork sources, such as social media sources, blogs, and other textualsources. The network sources are accessible from communications network102, such as the Internet. QA system 100 ingests such opinions andreviews and generates normalized sentiment scores pertaining to thevarious authors.

FIG. 4 depicts some of the data that is maintained in corpus 106utilized by QA system 100 to provide normalized-sentiment scores andanswers to sentiment-based questions. User profiles 360 include profiledata of the various authors and indicate individual author's propensity,or bias, towards or against a particular issue or opinion. User profiles360 can be seen as including importance factors 460 and the actualpersonality profile 470. As the name implies, importance factors arethose factors that have been shown to be important to a particularauthor and may be associated with particular sentiment-based words orphrases, while personality profile 470 is individual to a particularauthor and provides associations to electronic documents that weregathered to generate the importance factors of the author as well as thesubjective strength of the various importance factors (e.g., howstrongly the author has been shown to support a particular opinion,cause, issue, etc.). Personality profile clusters 495 are used by the QAsystem to group personality profiles based on the importance factors ofthe various authors included in a common group. Personality profileclusters 495 are also associated with sentiment-based words and phrases.

In one embodiment, author's personality profiles have a time componentthat indicates how the author's personality, and associatedsentiment-based words and phrases, has changed over time. For example,an author that is a political commentator may have had more liberalviewpoints when initially reporting and commenting on politicalviewpoints, but the author's personality may have shifted to being moremoderate, and even conservative, over the author's career. Thetime-based component provides further granularity that allows theauthor's viewpoints expressed in electronic documents to be matched tothe author's personality that was expressed during the same time periodas the electronic document. For example, an electronic document writtenearly in the author's career could be viewed as being written by anauthor with a liberal political personality profile, but anotherelectronic document written much later in the same author's career canbe viewed as being written by an author with a more conservativepolitical personality profile. Likewise, the normalized sentiment scoreswould reflect the time period during which the author wrote a particularopinion, review, article, post, or the like.

FIG. 5 is a depiction of a QA system pipeline utilized in providingqualitative answers based on normalized sentiment scores included in theQA system corpus. FIG. 5 is a component diagram depicting the variouscomponents of the QA system that answers questions posed by requestorsusing the multi-corpus knowledge base with weighting normalizedsentiment scores. Corpus 106 shows electronic documents 107 andnormalized sentiment scores 390 being utilized by the QA system. Corpus106 is updated using the corpus ingestion processing shown in FIG. 3,while normalized sentiment scores 390 are updated using the sentimentnormalization process shown in FIG. 3 as well as in FIGS. 7-10.

QA system pipeline 500 is broken down to depict many of the individualpipeline steps included in the QA system pipeline. Question 510 posed tothe QA system is depicted as a sentiment-based question, such as aquestion pertaining to qualitative and/or subjective characteristics. Anexample of a sentiment-based questions might be “what is the best placeto take a family for vacation?”

At step 525, the QA system pipeline performs the question and topicanalysis process. At step 530, the QA system pipeline performs thequestion decomposition process. At step 540, the QA system pipelineperforms the primary search process. At step 550, the QA system pipelineperforms the candidate answer generation process. At step 560, the QAsystem pipeline performs the candidates answer scoring process. At step570, the QA system pipeline performs the supporting evidence retrievalprocess. At step 580, the QA system pipeline performs the deep evidencescoring process. At step 590, the QA system pipeline performs the finalmerging and ranking process.

Many of the steps can utilize normalized sentiment scores 390. Two ofthe steps that utilize the normalized sentiment scores when respondingto sentiment-based questions are the candidate answer generation step(step 550) and the candidate answer scoring step (step 560). At step550, the pipeline's candidate answer generation process finds potentialanswers from ingested passages (corpus data store 106). Because of thesubjective nature of these passages, many of such passages have anassociated normalized sentiment score At step 560, the process performscandidates answer scoring. In step 560, passages with higher normalizedsentiment scores are given more weight than other passages.

FIG. 6 is a depiction of the clustering of personality profile insights,or clusters, 495. Various clusters are depicted along the left side ofthe diagram, including openness cluster 600, extraversion cluster 610,conscientiousness cluster 620, agreeableness cluster 630, neuroticismcluster 640, and any number of other clusters 650.

Sentiment words and phrases associated with each of the clusters isdepicted as data stores along the right side of the diagram. Each of thedata stores is associated with a different cluster. In the exampleshown, data store 602 includes sentiment words for “openness” and isassociated with openness cluster 600, data store 612 includes sentimentwords for “extraversion” and is associated with extraversion cluster610, data store 622 includes sentiment words for “conscientiousness” andis associated with conscientiousness cluster 620, data store 632includes sentiment words for “agreeableness” and is associated withagreeableness cluster 630, data store 642 includes sentiment words for“neuroticism” and is associated with neuroticism cluster 640, and datastore 652 includes other sentiment words and is associated with otherclusters 650. Any number of clusters and associated sentiment words canbe included in personality profile insights 495.

FIG. 7 is a depiction of a flowchart showing the logic used to analyzeand normalize sentiment data utilized in the QA system. FIG. 7processing commences at 700 and shows the steps taken by a process thatsentiment Normalization. At step 710, the process identifies the authorof document 320 that is being ingested into the QA system. At predefinedprocess 720, the process performs the Use Personality Insights to BuildPersonality Profile routine (see FIG. 8 and corresponding text forprocessing details). The author's personality profile is stored in datastore 360 along with the personality profiles of other authors whosework has been ingested into QA system 100.

At step 725, the process selects the first passage from document 320.The process determines as whether the selected passage includessentiment data, such as opinion data or qualitative data (decision 730).If the selected passage includes sentiment data, then decision 730branches to the ‘yes’ branch to process the sentiment data from thepassage. On the other hand, if the selected passage does not includesentiment data, then decision 730 branches to the ‘no’ branch whichloops back to continue scanning passages from the document. At step 740,the process identifies the author of the selected passage which might bea different author than the author of the overall document. At step 750,the process calculates a standard sentiment score of the sentiment wordsfound in the selected passage. Step 750 stores the standard sentimentscore in data store 340 and stores the sentiment words found in theselected passage in memory area 755. The process determines whether theauthor of the selected passage is the same as the author of the document(decision 760). If the author of the selected passage is the same as theauthor of the document, then decision 760 branches to the ‘yes’ branchbypassing predefined process 765. On the other hand, if the author ofthe selected passage is a different author, then decision 760 branchesto the ‘no’ branch to perform predefined process 765. At predefinedprocess 765, the process performs the Use Personality Insights to BuildPersonality Profile routine for the author of the selected passage (seeFIG. 8 and corresponding text for processing details).

At predefined process 770, the process performs the Grouped PersonalitySentiment (Group PS) routine (see FIG. 9 and corresponding text forprocessing details). This routine groups, or clusters, authorpersonality profiles based on similarities found in such profiles.Predefined process stores the personality insight (PI) score in memoryarea 775 and the group personality sentiment in memory area 780. Atpredefined process 785, the process performs the Normalize SentimentScore routine (see FIG. 10 and corresponding text for processingdetails). This routine normalizes the sentiment score based upon theauthor's profile and relationship with the sentiment words found in thepassage. This routine takes as inputs the standard sentiment word score,the sentiment words found in the passage (stored in memory area 755),the personality index (PI) score (stored in memory area 775), and thegroup personality sentiment (stored in memory area 780).

The process determines as to whether there are more passages in document320 to process (decision 790). If there are more passages to process,then decision 790 branches to the ‘yes’ branch which loops back to step725 to select and process the next passage. This looping continues untilthere are no more passages to process, at which point decision 790branches to the ‘no’ branch exiting the loop. FIG. 7 processingthereafter ends at 795.

FIG. 8 is a depiction of a flowchart showing the logic used to generateuser profile data over different periods of time. FIG. 8 processingcommences at 800 and shows the steps taken by a process that generatesof author personality profiles over one or more time periods. At step810, the process selects the first author from a list of authors thathave contributed writings that have been ingested by QA system 100. Atstep 820, the process captures user reviews, documents, posts, etc.written in a natural language context by the selected author. Thewritings are captured from network-accessible electronic documentsavailable via computer network 102, such as the Internet. The reviews,documents, posts, etc. that are captured are stored in data store 825for processing. At step 830, the process captures content from user'ssocial media sites and stores such content in data store 825 forprocessing. At step 835, the process selects the first time period formodeling analysis.

In one embodiment, multiple time periods are analyzed so that anauthor's personality as it evolves over time can be captured. Forexample, an author may have had liberal political views early in his orher career, but the same author may now be more moderate or evenconservative in his or her views. At step 840, the process executes auser modeling analysis of the author's written text and social mediacontent retrieved from data store 825 to identify personality traits ofthe selected author based on the author's language and style in thewritings of the author during selected time period. Step 840 furtheraggregates the author modeling analysis results over multiple reviewswritten by the selected author. At step 850, the process classifies theselected author's personality profile based upon identified personalitytraits during the selected time period. Step 850 stores the author'spersonality profile data in data store 360 and assigns the author to oneor more personality cluster 495. At step 860, the process executes usermodeling analysis of the user's written reviews and social media contentto identify importance factors of the selected author during theselected time period. These importance factors are also stored, orassociated, with the author's personality profile that is stored in datastore 360.

The process determines whether there are more time periods to analyzefor the selected author (decision 870). If there are more time periodsto analyze, then decision 870 branches to the ‘yes’ branch which loopsback to step 835 to select and process content written by the authorduring the next time period. This looping continues until there are nomore time periods to process, at which point decision 870 branches tothe ‘no’ branch exiting the loop. The process determines as to whetherthere are more authors to analyze (decision 880). If there are moreauthors to analyze, then decision 880 branches to the ‘yes’ branch whichloops back to step 810 to select and process the next author that hascontributed work ingested by QA system 100. This looping continues untilthere are no more authors to analyze, at which point decision 880branches to the ‘no’ branch exiting the loop. FIG. 8 processingthereafter ends at 890.

FIG. 9 is a depiction of a flowchart showing the logic used to identifyauthor's group personality sentiments over different time periods. FIG.9 processing commences at 900 and shows the steps taken by a processthat groups, or clusters, author personality sentiments (Group PS). Atstep 910, the process selects the first time period. At step 920, theprocess selects the first passage attributed to this author duringselected time period. At step 925, the process selects the firstsentiment-based word/phrase from the selected passage.

At step 930, the process matches the selected sentiment-based word(s) topersonality insight words that are associated with different personalityclusters (see FIG. 6 for example personality clusters and theirassociated sentiment words). The process determines whether a matchfound between the selected sentiment words and one of the personalityclusters (decision 940). If a match was found, then decision 940branches to the ‘yes’ branch to perform step 950. On the other hand, ifno match was found, then decision 940 branches to the ‘no’ branchbypassing step 950. At step 950, the process weights the selectedauthor's grouped personality sentiment during selected time period tothe group (cluster) from which matching sentiment-based word(s) werefound. The author's time-period based group personality sentiment scoresare stored in memory area 960.

The process determines whether there are more sentiment-based word(s) inthe selected passage to process (decision 970). If there are moresentiment-based word(s) in the selected passage to process, thendecision 970 branches to the ‘yes’ branch which loops back to step 925to select and process the next sentiment words from the selectedpassage. This looping continues until there are no more sentiment-basedword(s) in the selected passage to process, at which point decision 970branches to the ‘no’ branch exiting the loop.

The process determines whether there are more passages written by theauthor during the selected time period (decision 975). If there are morepassages written by the author during the selected time period, thendecision 975 branches to the ‘yes’ branch which loops back to step 920to select and process the next passage that was written during theselected time period. This looping continues until there are no morepassages that were written by the author during the selected timeperiod, at which point decision 975 branches to the ‘no’ branch exitingthe loop.

At step 980, the process analyzes the scored author time-period basedgroup personality sentiment from memory area 960 and identifies one ormore Group PS (clusters) to associate with the author during theselected time period. This personality sentiment cluster, or clusters,is/are stored in the author's time-period based personality profileinsights (clusters) 495. The process next determines whether there aremore time periods to process (decision 995). If there are more timeperiods to process, then decision 995 branches to the ‘yes’ branch whichloops back to step 910 to select and process the next time period asdescribed above. This looping continues until there are no more timeperiods to process, at which point decision 995 branches to the ‘no’branch exiting the loop. FIG. 9 processing thereafter returns to thecalling routine (see FIG. 7) at 995.

FIG. 10 is a depiction of a flowchart showing the logic used tonormalize sentiment scores utilized by the QA system. FIG. 10 processingcommences at 1000 and shows the steps taken by a process that normalizessentiment scores. At step 1010, the process selects the first sentimentword from the list of sentiment words that were identified in a passageand stored in memory area 755. At step 1020, the process checks GroupPersonality Sentiment 495 for this author during various time periods.The author's groups that are found by step 1020 are stored in memoryarea 1025. At step 1030, the process selects the first matching Group PSfrom memory area 1025.

The process determines whether the selected sentiment word is associatedwith the selected Group PS (decision 1040). If the selected sentimentword is associated with the selected Group PS, then decision 1040branches to the ‘yes’ branch for further processing to adjust thesentiment score based on the author's personality profile. On the otherhand, if the selected sentiment word is not associated with the selectedGroup PS, then decision 1040 branches to the ‘no’ branch bypassing steps1060 and 1070. When the selected sentiment word is associated with theselected Group PS, then process next determines whether the time periodof the selected Group PS matches the time period of the passage(decision 1050). If the time periods match, then decision 1050 branchesto the ‘yes’ branch to perform step 1060. On the other hand, if the timeperiods do not match, then decision 1050 branches to the ‘no’ branch toperform step 1070.

At step 1060, the process makes a larger adjustment of the sentimentscore (up or down) based on the author's personality profile beingassociated with the selected sentiment words during the relevant timeperiod. Step 1060 stores the adjusted score that indicates the author'srelationship with the sentiment word in memory area 1075. At step 1070,the process makes a smaller adjustment of sentiment score (up or down)based on author's personality being associated with the selectedsentiment words but during a different time period. Step 1070 stores theadjusted score that indicates the author's relationship with thesentiment word in memory area 1075.

The process determines whether there are more matching Group PSes storedin memory area 1025 that need to be processed (decision 1080). If thereare more matching Group PSes stored in memory area 1025 that need to beprocessed, then decision 1080 branches to the ‘yes’ branch which loopsback to step 1030 to select and process the next matching Group PS frommemory area 1025. This looping continues until there are no morematching Group PSes to process in memory area 1025, at which pointdecision 1080 branches to the ‘no’ branch exiting the loop.

At step 1085, the process computes a normalized score of the sentimentword based on the standard sentiment score (PI) of the sentiment wordthat is retrieved from data store 340 and the score pertaining to theauthor's relationship with word that is retrieved from memory area 1075.The normalized sentiment score of the sentiment word is stored in datastore 390.

The process determines as to whether there are more sentiment words toprocess from memory area 755 (decision 1090). If there are moresentiment words to process, then decision 1090 branches to the ‘yes’branch which loops back to step 1010 to select and process the nextsentiment word from memory area 755. This looping continues until thereare no more sentiment words to process, at which point decision 1090branches to the ‘no’ branch exiting the loop. FIG. 10 processingthereafter returns to the calling routine (see FIG. 7) at 1095.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. It will be understood by those with skill in the artthat if a specific number of an introduced claim element is intended,such intent will be explicitly recited in the claim, and in the absenceof such recitation no such limitation is present. For non-limitingexample, as an aid to understanding, the following appended claimscontain usage of the introductory phrases “at least one” and “one ormore” to introduce claim elements. However, the use of such phrasesshould not be construed to imply that the introduction of a claimelement by the indefinite articles “a” or “an” limits any particularclaim containing such introduced claim element to inventions containingonly one such element, even when the same claim includes theintroductory phrases “one or more” or “at least one” and indefinitearticles such as “a” or “an”; the same holds true for the use in theclaims of definite articles.

What is claimed is:
 1. A method implemented by an information handlingsystem that includes a processor and a memory accessible by theprocessor, the method comprising: analyzing a plurality of electronicdocument sets, each of the sets written by a different author, whereinthe analysis includes performing a normalized sentiment analysis of theplurality of electronic documents resulting in normalized sentimentscores pertaining to each of the authors that is stored in a data storeaccessible from a question answering (QA) system; receiving, at thequestion answering system, a sentiment-based question; and generating,by the QA system, a qualitative set of one or more candidate answersresponsive to the sentiment-based question, wherein the candidateanswers are based in part on the normalized sentiment scores retrievedfrom the data store.
 2. The method of claim 1 further comprising:ingesting a selected one of the plurality of electronic documents into acorpus utilized by the QA system; identifying an author of the selectedelectronic document; generating a sentiment score pertaining to atextual passage included in the selected electronic document; retrievinga personality profile corresponding to the identified author, whereinthe retrieved personality profile includes a personality insight basedupon previously ingested sentiments pertaining to the identified author;and adjusting the generated sentiment score based on the identifiedauthor's personality insight, wherein the adjusting results in thenormalized sentiment score that is stored in the data store.
 3. Themethod of claim 1 further comprising: generating a personality profilecorresponding to one or more of the authors, wherein the generation ofeach of the personality profiles comprises: capturing a set of passageswritten by each of the authors; performing an author modeling analysisof the set of passages, wherein the author modeling analysis identifiesone or more personality traits of each of the authors based onsentiment-based words found in the set of passages; and classifying eachof the authors' personality profiles into one or more personalityclusters, wherein each of the personality clusters is associated withone or more sentiment-based words.
 4. The method of claim 3 furthercomprising: ingesting a selected one of the plurality of electronicdocuments into a corpus utilized by the QA system; identifying an authorof the selected electronic document; generating a sentiment scorepertaining to a textual passage included in the selected electronicdocument; comparing one or more words found in the textual passage withthe sentiment-based words associated with the personality clusters;based on the comparison, identifying one or more matching personalityclusters; determining whether the identified author is associated withone of the identified personality clusters; and adjusting the generatedsentiment score based on the determination, wherein the adjustingresults in the normalized sentiment score that is stored in the datastore
 5. The method of claim 4 further comprising: identifying a timeperiod of the selected electronic document; determining whether theidentified author was associated with one of the personality clustersduring the identified time period; and further adjusting the generatedsentiment score based on the identified author's association with one ofthe matching personality clusters during the identified time period. 6.The method of claim 1 further comprising: ingesting a selected one ofthe plurality of electronic documents into a corpus utilized by the QAsystem; identifying an author of the selected electronic document;identifying a sentiment word included in the selected electronicdocument; determining a relationship of the identified sentiment wordwith the identified author; generating a standard sentiment scorepertaining to the selected electronic document; and computing thenormalized sentiment score pertaining to the selected electronicdocument based on determined relationship and the standard sentimentscore.
 7. The method of claim 6 wherein the determination of therelationship of the identified sentiment word with the identified authorfurther comprises: comparing the identified sentiment word to one ormore sentiment words associated with a personality profile that isassociated with the identified author, wherein the relationship isstronger when the comparison successfully matches the identifiedsentiment word to one of the associated sentiment words.
 8. Aninformation handling system comprising: one or more processors; a memorycoupled to at least one of the processors; and a set of computer programinstructions stored in the memory and executed by at least one of theprocessors in order to perform actions comprising: analyzing a pluralityof electronic document sets, each of the sets written by a differentauthor, wherein the analysis includes performing a normalized sentimentanalysis of the plurality of electronic documents resulting innormalized sentiment scores pertaining to each of the authors that isstored in a data store accessible from a question answering (QA) system;receiving, at the question answering system, a sentiment-based question;and generating, by the QA system, a qualitative set of one or morecandidate answers responsive to the sentiment-based question, whereinthe candidate answers are based in part on the normalized sentimentscores retrieved from the data store.
 9. The information handling systemof claim 8 wherein the actions further comprise: ingesting a selectedone of the plurality of electronic documents into a corpus utilized bythe QA system; identifying an author of the selected electronicdocument; generating a sentiment score pertaining to a textual passageincluded in the selected electronic document; retrieving a personalityprofile corresponding to the identified author, wherein the retrievedpersonality profile includes a personality insight based upon previouslyingested sentiments pertaining to the identified author; and adjustingthe generated sentiment score based on the identified author'spersonality insight, wherein the adjusting results in the normalizedsentiment score that is stored in the data store.
 10. The informationhandling system of claim 8 wherein the actions further comprise:generating a personality profile corresponding to one or more of theauthors, wherein the generation of each of the personality profilescomprises: capturing a set of passages written by each of the authors;performing an author modeling analysis of the set of passages, whereinthe author modeling analysis identifies one or more personality traitsof each of the authors based on sentiment-based words found in the setof passages; and classifying each of the authors' personality profilesinto one or more personality clusters, wherein each of the personalityclusters is associated with one or more sentiment-based words.
 11. Theinformation handling system of claim 10 wherein the actions furthercomprise: ingesting a selected one of the plurality of electronicdocuments into a corpus utilized by the QA system; identifying an authorof the selected electronic document; generating a sentiment scorepertaining to a textual passage included in the selected electronicdocument; comparing one or more words found in the textual passage withthe sentiment-based words associated with the personality clusters;based on the comparison, identifying one or more matching personalityclusters; determining whether the identified author is associated withone of the identified personality clusters; and adjusting the generatedsentiment score based on the determination, wherein the adjustingresults in the normalized sentiment score that is stored in the datastore
 12. The information handling system of claim 11 wherein theactions further comprise: identifying a time period of the selectedelectronic document; determining whether the identified author wasassociated with one of the personality clusters during the identifiedtime period; and further adjusting the generated sentiment score basedon the identified author's association with one of the matchingpersonality clusters during the identified time period.
 13. Theinformation handling system of claim 8 wherein the actions furthercomprise: ingesting a selected one of the plurality of electronicdocuments into a corpus utilized by the QA system; identifying an authorof the selected electronic document; identifying a sentiment wordincluded in the selected electronic document; determining a relationshipof the identified sentiment word with the identified author; generatinga standard sentiment score pertaining to the selected electronicdocument; and computing the normalized sentiment score pertaining to theselected electronic document based on determined relationship and thestandard sentiment score.
 14. The information handling system of claim13 wherein the determination of the relationship of the identifiedsentiment word with the identified author further comprises: comparingthe identified sentiment word to one or more sentiment words associatedwith a personality profile that is associated with the identifiedauthor, wherein the relationship is stronger when the comparisonsuccessfully matches the identified sentiment word to one of theassociated sentiment words.
 15. A computer program product stored in acomputer readable storage medium, comprising computer program code that,when executed by an information handling system, performs actionscomprising: analyzing a plurality of electronic document sets, each ofthe sets written by a different author, wherein the analysis includesperforming a normalized sentiment analysis of the plurality ofelectronic documents resulting in normalized sentiment scores pertainingto each of the authors that is stored in a data store accessible from aquestion answering (QA) system; receiving, at the question answeringsystem, a sentiment-based question; and generating, by the QA system, aqualitative set of one or more candidate answers responsive to thesentiment-based question, wherein the candidate answers are based inpart on the normalized sentiment scores retrieved from the data store.16. The computer program product of claim 15 wherein the actions furthercomprise: ingesting a selected one of the plurality of electronicdocuments into a corpus utilized by the QA system; identifying an authorof the selected electronic document; generating a sentiment scorepertaining to a textual passage included in the selected electronicdocument; retrieving a personality profile corresponding to theidentified author, wherein the retrieved personality profile includes apersonality insight based upon previously ingested sentiments pertainingto the identified author; and adjusting the generated sentiment scorebased on the identified author's personality insight, wherein theadjusting results in the normalized sentiment score that is stored inthe data store.
 17. The computer program product of claim 15 wherein theactions further comprise: generating a personality profile correspondingto one or more of the authors, wherein the generation of each of thepersonality profiles comprises: capturing a set of passages written byeach of the authors; performing an author modeling analysis of the setof passages, wherein the author modeling analysis identifies one or morepersonality traits of each of the authors based on sentiment-based wordsfound in the set of passages; and classifying each of the authors'personality profiles into one or more personality clusters, wherein eachof the personality clusters is associated with one or moresentiment-based words.
 18. The computer program product of claim 17wherein the actions further comprise: ingesting a selected one of theplurality of electronic documents into a corpus utilized by the QAsystem; identifying an author of the selected electronic document;generating a sentiment score pertaining to a textual passage included inthe selected electronic document; comparing one or more words found inthe textual passage with the sentiment-based words associated with thepersonality clusters; based on the comparison, identifying one or morematching personality clusters; determining whether the identified authoris associated with one of the identified personality clusters; andadjusting the generated sentiment score based on the determination,wherein the adjusting results in the normalized sentiment score that isstored in the data store
 19. The computer program product of claim 18wherein the actions further comprise: identifying a time period of theselected electronic document; determining whether the identified authorwas associated with one of the personality clusters during theidentified time period; and further adjusting the generated sentimentscore based on the identified author's association with one of thematching personality clusters during the identified time period.
 20. Thecomputer program product of claim 15 wherein the actions furthercomprise: ingesting a selected one of the plurality of electronicdocuments into a corpus utilized by the QA system; identifying an authorof the selected electronic document; identifying a sentiment wordincluded in the selected electronic document; determining a relationshipof the identified sentiment word with the identified author, wherein thedetermination includes comparing the identified sentiment word to one ormore sentiment words associated with a personality profile that isassociated with the identified author, wherein the relationship isstronger when the comparison successfully matches the identifiedsentiment word to one of the associated sentiment words; generating astandard sentiment score pertaining to the selected electronic document;and computing the normalized sentiment score pertaining to the selectedelectronic document based on determined relationship and the standardsentiment score.